FeaturedUdemy
Feature Engineering for NLP in Python
4.9(145)29k studentsINTERMEDIATEEnglish
Korshub's take
The Korshub Take
This intermediate NLP course teaches you how to convert raw text into structured numerical representations suitable for machine learning models. Using spaCy and scikit-learn, you will practice extracting linguistic features and building real-world recommendation and sentiment systems.
Who It's For
Data scientists and Python developers looking to master text feature extraction for natural language processing applications.
Key Takeaways
- Linguistic Tagging & Metrics: Perform POS tagging, Named Entity Recognition (NER), and compute readability scores using spaCy.
- Vectorization Models: Implement n-gram and TF-IDF vectorizers using scikit-learn.
- Similarity & Recommenders: Compute document similarity metrics to predict movie review sentiment and build recommendation systems.
About this course
In this course, you will learn techniques that will allow you to extract useful information from text and process them into a format suitable for applying ML models. More specifically, you will learn about POS tagging, named entity recognition, readability scores, the n-gram and tf-idf models, and how to implement them using scikit-learn and spaCy. You will also learn to compute how similar two documents are to each other. In the process, you will predict the sentiment of movie reviews and build movie and Ted Talk recommenders. Following the course, you will be able to engineer critical features out of any text and solve some of the most challenging problems in data science!
Price
Free
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